Abstract: The present invention is based on the idea of determining certain molecular lipids from a subject's blood sample, for example from serum or plasma sample, and based on the amounts of the determined lipids determining the amount of liver fat and/or diagnosing NAFLD in the subject. More specifically the subjects with elevated liver fat amount and NAFLD are characterized by elevated triglycerides with low carbon number and double bond content in the blood sample. Lysophosphatidylcholines, ether phospholipids, sphingomyelins and PUFA-containing phospholipids are diminished in the blood samples of subjects with an elevated liver fat amount and NAFLD. The method of the present invention can be further used for monitoring the subject's response to the treatment of NAFLD or to the treatment of lowering of the liver fat amount in the subject.

Abstract: Method/system for visualizing biological information. The system receives a user query relating to biological information and determines which database contains related biological information. A database query is sent to the database and the result indicates biological/chemical entities and relations. A network is created based on the result; biological/chemical entities are mapped to network nodes and relations to network connections. A distance matrix indicates a multi-dimensional distance for several pairs of network nodes. A dimensionality reduction function is adjusted based on a research context, to bias the search toward a relevant focus. The number of dimensions in the distance matrix is lowered by the dimensionality reduction function. Neighbors of a selected node are searched (2-18) based on the distance matrix to elucidate a biological role of the selected node. A re-created network is visualized (2-20) based on the adjusted dimensionality reduction function.

Abstract: Method/system for visualizing biological information. The system receives a user query relating to biological information and determines which database contains related biological information. A database query is sent to the database and the result indicates biological/chemical entities and relations. A network is created based on the result; biological/chemical entities are mapped to network nodes and relations to network connections. A distance matrix indicates a multi-dimensional distance for several pairs of network nodes. A dimensionality reduction function is adjusted based on a research context, to bias the search toward a relevant focus. The number of dimensions in the distance matrix is lowered by the dimensionality reduction function. Neighbours of a selected node are searched (2-18) based on the distance matrix to elucidate a biological role of the selected node. A re-created network is visualised (2-20) based on the adjusted dimensionality reduction function.